New PDF release: AI 2010: Advances in Artificial Intelligence: 23rd

By Jiuyong Li

ISBN-10: 3642174310

ISBN-13: 9783642174315

This publication constitutes the refereed lawsuits of the twenty third Australasian Joint convention on man made Intelligence, AI 2010, held in Adelaide, Australia, in December 2010. The fifty two revised complete papers offered have been rigorously reviewed and chosen from 112 submissions. The papers are equipped in topical sections on wisdom illustration and reasoning; facts mining and data discovery; computer studying; statistical studying; evolutionary computation; particle swarm optimization; clever agent; seek and making plans; usual language processing; and AI functions.

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Read or Download AI 2010: Advances in Artificial Intelligence: 23rd Australasian Joint Conference, Adelaide, Australia, December 7-10, 2010. Proceedings PDF

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Additional resources for AI 2010: Advances in Artificial Intelligence: 23rd Australasian Joint Conference, Adelaide, Australia, December 7-10, 2010. Proceedings

Example text

P1 ⇒ [s; c1]e .. .. P2 .. P3 ⇒ [s; c1]e ⇒ [s; c1]e ⇒ [s; c1](e ∗ e) ⇒ [s; c1](e ∗ e ∗ e) [s; c1](e ∗ e ∗ e) ⇒ [s; c1]r ⇒ [s; c1]r In SLL− , [s; c1]reject cannot be proven if more than k errors arise because SLL− does not have the expressive power of SLL. Acknowledgments. We are partially supported by the Japanese Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Young Scientists (B) 20700015 and (B) 20700147. References 1. : Linear logic. Theoretical Computer Science 50, 1–102 (1987) 2.

We therefore conclude that neither the typical application of the AGM theory nor the belief update theory is the right answer for the problem here. In addition, for this example, it is evident that to express how events unfold, we require a representation of belief where the element of time is explicitly captured. In this paper, we present a temporal model for beliefs called histories that are semantic objects analogous to the models in PLTL (Propositional Linear Temporal Logic) [12]. We then develop a temporal belief change framework which is an adaptation of the theory of κfunctions to the histories.

Classical logic is not resource-sensitive. In order to discuss certain real and practical examples, the resource descriptions should be more fine-grained and expressive and capable of conveying additional information. For example, the following expression may be necessary for a practical situation: [teashop ; john](coin ∗ coin ∗ coin → [1min ; 1min]coffee ∗ J. ): AI 2010, LNAI 6464, pp. 22–31, 2010. c Springer-Verlag Berlin Heidelberg 2010 Resource-Sensitive Reasoning with Sequential Information 23 [1min]water).

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AI 2010: Advances in Artificial Intelligence: 23rd Australasian Joint Conference, Adelaide, Australia, December 7-10, 2010. Proceedings by Jiuyong Li

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